{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,24]],"date-time":"2025-10-24T08:15:17Z","timestamp":1761293717835,"version":"build-2065373602"},"reference-count":43,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2018,3,22]],"date-time":"2018-03-22T00:00:00Z","timestamp":1521676800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61573313"],"award-info":[{"award-number":["61573313"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Online water-quality anomaly detection, classification, and identification based on multi-source information fusion","award":["U1509208"],"award-info":[{"award-number":["U1509208"]}]},{"name":"Research on big data analysis and cloud service of urban drinking water network safety"},{"name":"Research on intelligent management and long-effective mechanism for river regulation and maintenance","award":["2015C03G2010034"],"award-info":[{"award-number":["2015C03G2010034"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In water-quality, early warning systems and qualitative detection of contaminants are always challenging. There are a number of parameters that need to be measured which are not entirely linearly related to pollutant concentrations. Besides the complex correlations between variable water parameters that need to be analyzed also impairs the accuracy of quantitative detection. In aspects of these problems, the application of least-squares support vector machines (LS-SVM) is used to evaluate the water contamination and various conventional water quality sensors quantitatively. The various contaminations may cause different correlative responses of sensors, and also the degree of response is related to the concentration of the injected contaminant. Therefore to enhance the reliability and accuracy of water contamination detection a new method is proposed. In this method, a new relative response parameter is introduced to calculate the differences between water quality parameters and their baselines. A variety of regression models has been examined, as result of its high performance, the regression model based on genetic algorithm (GA) is combined with LS-SVM. In this paper, the practical application of the proposed method is considered, controlled experiments are designed, and data is collected from the experimental setup. The measured data is applied to analyze the water contamination concentration. The evaluation of results validated that the LS-SVM model can adapt to the local nonlinear variations between water quality parameters and contamination concentration with the excellent generalization ability and accuracy. The validity of the proposed approach in concentration evaluation for potassium ferricyanide is proven to be more than 0.5 mg\/L in water distribution systems.<\/jats:p>","DOI":"10.3390\/s18040938","type":"journal-article","created":{"date-parts":[[2018,3,22]],"date-time":"2018-03-22T05:14:55Z","timestamp":1521695695000},"page":"938","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Application of Least-Squares Support Vector Machines for Quantitative Evaluation of Known Contaminant in Water Distribution System Using Online Water Quality Parameters"],"prefix":"10.3390","volume":"18","author":[{"given":"Kexin","family":"Wang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China"}]},{"given":"Xiang","family":"Wen","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China"}]},{"given":"Dibo","family":"Hou","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China"}]},{"given":"Dezhan","family":"Tu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China"}]},{"given":"Naifu","family":"Zhu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China"}]},{"given":"Pingjie","family":"Huang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China"}]},{"given":"Guangxin","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China"}]},{"given":"Hongjian","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China"}]}],"member":"1968","published-online":{"date-parts":[[2018,3,22]]},"reference":[{"key":"ref_1","unstructured":"Hasan, J. (2005). Technologies and Techniques for Early Warning Systems to Monitor and Evaluate Drinking Water Quality A State-of-the-Art Review."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"741","DOI":"10.1016\/j.watres.2010.08.049","article-title":"Advances in on-line drinking water quality monitoring and early warning systems","volume":"45","author":"Storey","year":"2011","journal-title":"Water Res."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1390","DOI":"10.1016\/j.scitotenv.2017.08.232","article-title":"Applying high-frequency surrogate measurements and a wavelet-ANN model to provide early warnings of rapid surface water quality anomalies","volume":"610","author":"Shi","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_4","unstructured":"Hart, D.B., Mckenna, S.A., Murray, R., and Haxton, T. (2010). Combining Water Quality and Operational Data for Improved Event Detection, ASCE Library."},{"key":"ref_5","unstructured":"He, H. (2013). Research on Multi-Sensor Data Fusion for Water Quality Events Detection, Zhejiang University."},{"key":"ref_6","unstructured":"Murray, R., Haxton, T., McKenna, S.A., Hart, D.B., Klise, K., Koch, M., Vugrin, E.D., Martin, S., Wilson, M., and Cruze, V.A. (2010). Water Quality Event Detection Systems for Drinking Water Contamination Warning Systems Development, Testing, and Application of Canary."},{"key":"ref_7","unstructured":"Allgeier, S., Murray, R., Mckenna, S., and Shalvi, D. (2005). Overview of Event Detection Systems for Water Sentinel."},{"key":"ref_8","unstructured":"Mahoney, M.V. (2003). A Machine Learning Approach to Detecting Attacks by Identifying Anomalies in Network Traffic. [Ph.D. Thesis, Florida Institute of Technology]."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Raciti, M., Cucurull, J., and Nadjm-Tehrani, S. (2012). Anomaly Detection in Water Management Systems Critical Infrastructure Protection, Springer.","DOI":"10.1007\/978-3-642-28920-0_6"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"75","DOI":"10.5053\/ekoloji.2011.7812","article-title":"Detection of drinking water quality using CMAC based artificial neural Networks","volume":"20","author":"Bucak","year":"2011","journal-title":"Ekoloji"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1250013","DOI":"10.1142\/S1469026812500137","article-title":"Performance evaluation of three pattern classification techniques used for water quality monitoring","volume":"11","author":"Bouamar","year":"2012","journal-title":"Int. J. Comput. Intell. Appl."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"055801","DOI":"10.1088\/0957-0233\/24\/5\/055801","article-title":"Detection of water-quality contamination events based on multi-sensor fusion using an extented Dempster-Shafer method","volume":"24","author":"Hou","year":"2013","journal-title":"Meas. Sci. Technol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1061\/(ASCE)WR.1943-5452.0000094","article-title":"Distributed sensor fusion in water quality event detection","volume":"137","author":"Koch","year":"2010","journal-title":"J. Water Resour. Plan. Manag."},{"key":"ref_14","unstructured":"Hall, J., and Szabo, J. (2005). Water Sentinel Online Water Quality Monitoring as an Indicator of Drinking Water Contamination."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.jhydrol.2004.11.010","article-title":"Applicability of neuro-fuzzy techniques in predicting ground-water vulnerability: A GIS-based sensitivity analysis","volume":"309","author":"Dixon","year":"2005","journal-title":"J. Hydrol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.envint.2012.11.007","article-title":"Water quality analysis in rivers with non-parametric probability distributions and fuzzy inference systems: Application to the Cauca River, Colombia","volume":"52","author":"Osorio","year":"2013","journal-title":"Environ. Int."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/j.snb.2012.02.092","article-title":"Performance of an electronic tongue during monitoring 2-methylisoborneol and geosmin in water samples","volume":"171","author":"Braga","year":"2012","journal-title":"Sens. Actuators B Chem."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1080\/03067310701441005","article-title":"Remote environmental monitoring employing a potentiometric electronic tongue","volume":"88","author":"Alegret","year":"2008","journal-title":"Int. J. Environ. Anal. Chem."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Carb\u00f3, N., L\u00f3pez Carrero, J., Garcia-Castillo, F.J., Tormos, I., Olivas, E., Folch, E., Alca\u00f1iz Fillol, M., Soto, J., Mart\u00ednez-M\u00e1\u00f1ez, R., and Mart\u00ednez-Bisbal, M.C. (2017). Quantitative Determination of Spring Water Quality Parameters via Electronic Tongue. Sensors, 18.","DOI":"10.3390\/s18010040"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1397","DOI":"10.1016\/j.watres.2007.10.009","article-title":"Modeling and testing of reactive contaminant transport in drinking water pipes: Chlorine response and implications for online contaminant detection","volume":"42","author":"Yang","year":"2008","journal-title":"Water Res."},{"key":"ref_21","first-page":"1","article-title":"Model-Based Real-Time Detection of Contamination Events","volume":"2008","author":"Shang","year":"2008","journal-title":"Water Distrib. Syst. Anal."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Granata, F., Papirio, S., Esposito, G., Gargano, R., and de Marinis, G. (2017). Machine Learning Algorithms for the Forecasting of Wastewater Quality Indicators. Water, 9.","DOI":"10.3390\/w9020105"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.aca.2011.07.027","article-title":"Support vector machines in water quality management","volume":"703","author":"Singh","year":"2011","journal-title":"Anal. Chim. Acta"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Granata, F., Gargano, R., and de Marinis, G. (2016). Support vector regression for rainfall-runoff modeling in urban Drainage: A comparison with the EPA\u2019s storm water management model. Water, 8.","DOI":"10.3390\/w8030069"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"704","DOI":"10.1016\/j.jhydrol.2006.01.021","article-title":"Support vector regression for real-time flood stage forecasting","volume":"328","author":"Yu","year":"2006","journal-title":"J. Hydrol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1016\/j.jhydrol.2010.12.041","article-title":"A wavelet-support vector machine conjunction model for monthly streamflow forecasting","volume":"399","author":"Kisi","year":"2011","journal-title":"J. Hydrol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"8368","DOI":"10.1016\/j.eswa.2008.10.061","article-title":"Application of least square support vector machines in the prediction of Aeration performance of plunging over fall jets from weirs","volume":"36","author":"Baylar","year":"2009","journal-title":"Expert Syst. Appl."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"458","DOI":"10.1016\/j.mcm.2011.11.021","article-title":"A hybrid approach of support vector regression with genetic algorithm optimization for aquaculture water quality prediction","volume":"58","author":"Liu","year":"2013","journal-title":"Math. Comput. Model."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"7624","DOI":"10.1016\/j.eswa.2008.09.053","article-title":"Generalization performance of support vector machines and neural networks in runoff modeling","volume":"36","author":"Behzad","year":"2009","journal-title":"Expert Syst. Appl."},{"key":"ref_30","unstructured":"Vapnik, V.N. (1998). Statistical learning theory. Adaptive and Learning Systems for Signal Processing, Communications and Control Series, John Wiley & Sons."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1023\/A:1018628609742","article-title":"Least squares support vector machine classifiers","volume":"9","author":"Suykens","year":"1999","journal-title":"Neural Process. Lett."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1667","DOI":"10.1162\/089976603321891855","article-title":"Asymptotic behaviors of support vector machines with Gaussian kernel","volume":"15","author":"Keerthi","year":"2003","journal-title":"Neural Comput."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Wen, X. (2016). Research on Online Quantitative Analysis of Accidental Contaminant in Urban Water Distribution System, Zhejiang University.","DOI":"10.1109\/I2MTC.2016.7520376"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Cawley, G.C. (2006, January 16\u201321). Leave-one-out cross-validation based model selection criteria for weighted LS-SVMs. Proceedings of the International Joint Conference on Neural Networks, Vancouver, BC, Canada.","DOI":"10.1109\/IJCNN.2006.246634"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1016\/j.ecolmodel.2007.10.005","article-title":"Three way K-fold cross-validation of resource selection functions","volume":"212","author":"Wiens","year":"2008","journal-title":"Ecol. Model."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Suykens, J.A.K., Van Gestel, T., De Brabanter, J., De Moor, B., and Vandewalle, J. (2002). Least Squares Support Vector Machines, World Scientific.","DOI":"10.1142\/5089"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"320","DOI":"10.1109\/TSMCB.2009.2020435","article-title":"Coupled Simulated Annealing","volume":"40","author":"Suykens","year":"2010","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"2133","DOI":"10.1142\/S0218127401003371","article-title":"Intelligence and cooperative search by coupled local minimizers","volume":"11","author":"Suykens","year":"2001","journal-title":"Int. J. Bifurc. Chaos"},{"key":"ref_39","unstructured":"De Brabanter, K., Karsmakers, P., Ojeda, F., Alzate, C., De Brabanter, J., Pelckmans, K., De Moor, B., Vandewalle, J., and Suykens, J.A. (2010). LS-SVMlab Toolbox User\u2019s Guide: Version 1.7, Katholieke Universiteit Leuven."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"2494","DOI":"10.1016\/j.jenvman.2009.01.021","article-title":"Real-time contaminant detection and classification in a drinking water pipe using conventional water quality sensors: Techniques and experimental results","volume":"90","author":"Yang","year":"2009","journal-title":"Environ. Manag. J."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1002\/j.1551-8833.2007.tb07847.x","article-title":"On-line water quality parameters as indicators of distribution system contamination","volume":"99","author":"Hall","year":"2007","journal-title":"Am. Water Works Assoc."},{"key":"ref_42","unstructured":"Szabo, J.G., Hall, J.S., and Meiners, G. (2007). Water Quality Sensor Responses to Contamination in a Single Pass Water Distribution System Simulator, Water Information Sharing and Analysis Center (WaterISAC). EPA\/600\/R-07\/001."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"4496","DOI":"10.1007\/s11356-012-1406-y","article-title":"An early warning and control system for urban, drinking water quality protection: China\u2019s experience","volume":"20","author":"Hou","year":"2013","journal-title":"Environ. Sci. Pollut. Res."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/4\/938\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T14:58:03Z","timestamp":1760194683000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/4\/938"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,3,22]]},"references-count":43,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2018,4]]}},"alternative-id":["s18040938"],"URL":"https:\/\/doi.org\/10.3390\/s18040938","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2018,3,22]]}}}